A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System

Byung Jun Kim; Dong Ah Shin; Jaehoon Sim; Woo Sang Cho; So Yoon Kwon; Gil Joon Suh; Kyung Su Kim; Taegyun Kim; Jung Chan Lee

doi:10.1007/978-3-031-44851-5_26

A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System

Byung Jun Kim, Dong Ah Shin, Jaehoon Sim, Woo Sang Cho, So Yoon Kwon, Gil Joon Suh, Kyung Su Kim, Taegyun Kim, Jung Chan Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

During Cardio Pulmonary Resuscitation (CPR), appropriate heart compression affects the quality of CPR, which is directly related to the patient’s life. Therefore, it is important to accurately judge the quality of CPR. Therefore, it is important to accurately judge the quality of CPR. Until now, there have been studies on bio signal-based CPR feedback systems such as EtCO₂ (End tidal CO₂, EtCO₂), Photoplethysmography (PPG). However, it is not possible to provide an accurate basis for improvement in compression. Therefore, in this study, a machine learning-based CBV (Carotid Blood Volume) classification model was developed for various bio-signal data. In the results, Sensitivity, Specificity, Precision, and Accuracy had values of 0.91, 0.97, 0.94, and 0.95, respectively, and showed high classification performance. Therefore, the CBV classification model presented in this study will be able to become a model based on a feedback system that can intuitively judge the quality of current CPR.

Original language	English
Title of host publication	Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023
Editors	Soon-Geul Lee, Jinung An, Nak Young Chong, Marcus Strand, Joo H. Kim
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	345-351
Number of pages	7
ISBN (Print)	9783031448508
DOIs	https://doi.org/10.1007/978-3-031-44851-5_26
Publication status	Published - 2024
Event	18th International Conference on Intelligent Autonomous Systems, IAS18 2023 - Suwon, Korea, Republic of Duration: 4 Jul 2023 → 7 Jul 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	795
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	18th International Conference on Intelligent Autonomous Systems, IAS18 2023
Country/Territory	Korea, Republic of
City	Suwon
Period	4/07/23 → 7/07/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Bio signals
Carotid blood volume
Classification
Machine learning

Access to Document

10.1007/978-3-031-44851-5_26

Cite this

Kim, B. J., Shin, D. A., Sim, J., Cho, W. S., Kwon, S. Y., Suh, G. J., Kim, K. S., Kim, T., & Lee, J. C. (2024). A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System. In S.-G. Lee, J. An, N. Y. Chong, M. Strand, & J. H. Kim (Eds.), Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023 (pp. 345-351). (Lecture Notes in Networks and Systems; Vol. 795). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44851-5_26

Kim, Byung Jun ; Shin, Dong Ah ; Sim, Jaehoon et al. / A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System. Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023. editor / Soon-Geul Lee ; Jinung An ; Nak Young Chong ; Marcus Strand ; Joo H. Kim. Springer Science and Business Media Deutschland GmbH, 2024. pp. 345-351 (Lecture Notes in Networks and Systems).

@inproceedings{10aa74ccae124d8c8aa5a0668441e3bf,

title = "A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System",

abstract = "During Cardio Pulmonary Resuscitation (CPR), appropriate heart compression affects the quality of CPR, which is directly related to the patient{\textquoteright}s life. Therefore, it is important to accurately judge the quality of CPR. Therefore, it is important to accurately judge the quality of CPR. Until now, there have been studies on bio signal-based CPR feedback systems such as EtCO2 (End tidal CO2, EtCO2), Photoplethysmography (PPG). However, it is not possible to provide an accurate basis for improvement in compression. Therefore, in this study, a machine learning-based CBV (Carotid Blood Volume) classification model was developed for various bio-signal data. In the results, Sensitivity, Specificity, Precision, and Accuracy had values of 0.91, 0.97, 0.94, and 0.95, respectively, and showed high classification performance. Therefore, the CBV classification model presented in this study will be able to become a model based on a feedback system that can intuitively judge the quality of current CPR.",

keywords = "Bio signals, Carotid blood volume, Classification, Machine learning",

author = "Kim, {Byung Jun} and Shin, {Dong Ah} and Jaehoon Sim and Cho, {Woo Sang} and Kwon, {So Yoon} and Suh, {Gil Joon} and Kim, {Kyung Su} and Taegyun Kim and Lee, {Jung Chan}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 18th International Conference on Intelligent Autonomous Systems, IAS18 2023 ; Conference date: 04-07-2023 Through 07-07-2023",

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Kim, BJ, Shin, DA, Sim, J, Cho, WS, Kwon, SY, Suh, GJ, Kim, KS, Kim, T & Lee, JC 2024, A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System. in S-G Lee, J An, NY Chong, M Strand & JH Kim (eds), Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023. Lecture Notes in Networks and Systems, vol. 795, Springer Science and Business Media Deutschland GmbH, pp. 345-351, 18th International Conference on Intelligent Autonomous Systems, IAS18 2023, Suwon, Korea, Republic of, 4/07/23. https://doi.org/10.1007/978-3-031-44851-5_26

A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System. / Kim, Byung Jun; Shin, Dong Ah; Sim, Jaehoon et al.
Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023. ed. / Soon-Geul Lee; Jinung An; Nak Young Chong; Marcus Strand; Joo H. Kim. Springer Science and Business Media Deutschland GmbH, 2024. p. 345-351 (Lecture Notes in Networks and Systems; Vol. 795).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Shin, Dong Ah

AU - Sim, Jaehoon

AU - Cho, Woo Sang

AU - Kwon, So Yoon

AU - Suh, Gil Joon

AU - Kim, Kyung Su

AU - Kim, Taegyun

AU - Lee, Jung Chan

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - During Cardio Pulmonary Resuscitation (CPR), appropriate heart compression affects the quality of CPR, which is directly related to the patient’s life. Therefore, it is important to accurately judge the quality of CPR. Therefore, it is important to accurately judge the quality of CPR. Until now, there have been studies on bio signal-based CPR feedback systems such as EtCO2 (End tidal CO2, EtCO2), Photoplethysmography (PPG). However, it is not possible to provide an accurate basis for improvement in compression. Therefore, in this study, a machine learning-based CBV (Carotid Blood Volume) classification model was developed for various bio-signal data. In the results, Sensitivity, Specificity, Precision, and Accuracy had values of 0.91, 0.97, 0.94, and 0.95, respectively, and showed high classification performance. Therefore, the CBV classification model presented in this study will be able to become a model based on a feedback system that can intuitively judge the quality of current CPR.

AB - During Cardio Pulmonary Resuscitation (CPR), appropriate heart compression affects the quality of CPR, which is directly related to the patient’s life. Therefore, it is important to accurately judge the quality of CPR. Therefore, it is important to accurately judge the quality of CPR. Until now, there have been studies on bio signal-based CPR feedback systems such as EtCO2 (End tidal CO2, EtCO2), Photoplethysmography (PPG). However, it is not possible to provide an accurate basis for improvement in compression. Therefore, in this study, a machine learning-based CBV (Carotid Blood Volume) classification model was developed for various bio-signal data. In the results, Sensitivity, Specificity, Precision, and Accuracy had values of 0.91, 0.97, 0.94, and 0.95, respectively, and showed high classification performance. Therefore, the CBV classification model presented in this study will be able to become a model based on a feedback system that can intuitively judge the quality of current CPR.

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Kim BJ, Shin DA, Sim J, Cho WS, Kwon SY, Suh GJ et al. A Machine Learning-Based Blood Volume Classification Model for Cardiopulmonary Resuscitation Robot Feedback System. In Lee SG, An J, Chong NY, Strand M, Kim JH, editors, Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 345-351. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-44851-5_26